基于PCA-BPNN的桥梁爆炸荷载时程预测  

Prediction of blast loads on bridge girders based on PCA-BPNN

作　　者：杜晓庆[1] 何益平 邱涛[1] 程帅[2] 张德志[2] DU Xiaoqing;HE Yiping;QIU Tao;CHENG Shuai;ZHANG Dezhi(School of Mechanics and Engineering Science,Shanghai University,Shanghai 200444,China;Northwest Institute of Nuclear Technology,Xi’an 710024,Shaanxi,China)

机构地区：[1]上海大学力学与工程科学学院,上海200444 [2]西北核技术研究所,陕西西安710024

出　　处：《爆炸与冲击》2025年第3期77-91,共15页Explosion and Shock Waves

摘　　要：人工智能方法是预测爆炸荷载的新手段,但现有方法主要用于预测爆炸冲击波的超压峰值或冲量,而用于预测反射超压时程的研究不多。针对这一问题,以平面冲击波绕射桥梁主梁为对象,提出了一种基于主成分分析(principal components analysis,PCA)和误差反向传播神经网络(backpropagation neural network,BPNN)的桥梁爆炸冲击波反射超压时程预测模型。该预测模型利用PCA降维处理时程数据,基于多任务学习的BPNN算法,提出了考虑超压峰值和冲量峰值影响的损失函数,使模型能有效预测不同入射超压下的桥梁冲击波荷载时程。通过分析多任务学习模型、多输入单输出模型和多输入多输出模型等3种BPNN模型,发现多任务学习模型的预测精度最高,而多输入多输出模型难以有效适应当前预测任务需求。采用多任务学习模型预测得到的桥梁表面各测点位置的反射超压时程、超压峰值精度较高,决定系数R2分别为0.792和0.987,作用在箱梁上的合力时程和扭矩时程预测值也与数值模拟值较为吻合。同时,该模型对内插值预测的表现优于外推值预测,但其在预测外推值方面同样展现出了一定的能力。Facing the challenges on the accurate and effective prediction under extreme loads,machine learning has gradually demonstrated its potential to replace traditional methods.Existing approaches primarily focus on predicting the peak overpressure or impulse of explosive shock waves,with limited research on predicting the reflected overpressure time history.Load-time history prediction encompasses not only the peak overpressure but also embraces various multi-dimensional information including duration,waveform,and impulse,thereby offering a more comprehensive depiction of the dynamic temporal and spatial characteristics of shock waves.To address this issue,a prediction model for bridge surface reflected overpressure time history is proposed,targeting a planar shock wave diffracting around a bridge section.This model is based on principal component analysis(PCA)and back propagation neural network(BPNN)algorithm with multi-task learning.A loss function considering the impact of peak overpressure and maximum impulse is introduced to fully consider the potential correlations between different modes after PCA dimension reduction.This enables the model to effectively predict bridge shock wave load time histories under varying incident overpressure.Through the analysis of three types of BPNN models,multi-task learning model,multi-input single-output model,and multi-input multi-output model.It was found that the multitask learning model has the highest prediction accuracy,while the multi-input multi-output model struggles to effectively adapt to the current predictive task requirements.The multitask learning model,used for predicting,achieves high precision in forecasting the time history of reflected overpressure at various measurement points on the bridge surface and the peak overpressure values,with R2 values of 0.792 and 0.987.It also closely matches the simulation values in predicting the time history of combined forces and torque acting on the box girder.Additionally,this model performs better in interpolative value pr

关 键 词：爆炸荷载预测 反射超压时程 误差反向传播神经网络 主成分分析 多任务学习 

分 类 号：O383[理学—流体力学]